When organizations build AI infrastructure, GPUs usually get all the attention.
Teams invest in the latest accelerators, add high speed networking, and expect training jobs to scale effortlessly. Yet many AI clusters deliver disappointing performance despite having powerful hardware.
The surprising part?
The GPUs are often idle.
GPU monitoring dashboards may show utilization dropping to 20%, 10%, or even 0% between bursts of activity. At first glance, this looks like a GPU problem, but in most cases it isn’t.
